Jupyter Book

### read in data and activate the environmentimport pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
import statsmodels.formula.api as smf
import pandas as pd
import plotly.graph_objects as go

#setting wd to where the data is
import os
os.chdir('C:/Users/U727148/Desktop/DATA/REVIEW/')

df_sankey_models_transformed = pd.read_excel('dfsankeymodels_updated_20250424.xlsx')

import plotly.graph_objects as go
import pandas as pd

# Assuming df_sankey_models_transformed already exists with 'gm', 'mit', 'oa'

# 1. Create unique labels
labels = list(pd.concat([
    df_sankey_models_transformed['gm'], 
    df_sankey_models_transformed['mit'], 
    df_sankey_models_transformed['oa']
]).unique())

# 2. Map labels to indices
label_map = {label: idx for idx, label in enumerate(labels)}

# 3. Define sources, targets, and values
sources = df_sankey_models_transformed['gm'].map(label_map).tolist() + \
          df_sankey_models_transformed['mit'].map(label_map).tolist()

targets = df_sankey_models_transformed['mit'].map(label_map).tolist() + \
          df_sankey_models_transformed['oa'].map(label_map).tolist()

values = df_sankey_models_transformed['gm_count'].tolist() + \
         df_sankey_models_transformed['oa_count'].tolist()

# 4. Generate pastel colors
import random

def pastel_color():
    r = lambda: random.randint(100, 255)
    return f'rgba({r()},{r()},{r()},0.6)'

label_to_color = {label: pastel_color() for label in labels}
node_color_list = [label_to_color[label] for label in labels]

# 5. Set link colors by source node's color (first half) and middle node (second half)
link_colors = [label_to_color[df_sankey_models_transformed['gm'].iloc[i]] 
               for i in range(len(df_sankey_models_transformed))] + \
              [label_to_color[df_sankey_models_transformed['mit'].iloc[i]] 
               for i in range(len(df_sankey_models_transformed))]

# 7. Create the figure
fig = go.Figure(data=[go.Sankey(
    node=dict(
        pad=15,
        thickness=20,
        line=dict(color="black", width=0.9),
        label=labels,
        color=node_color_list,
        align="left"
    ),
    link=dict(
        source=sources,
        target=targets,
        value=values,
        color=link_colors,
        customdata=df_sankey_models_transformed['nameyear'].tolist() * 2,
        hovertemplate='%{customdata}<extra></extra>'
    )
)])

fig.update_layout(
    title_text="Sankey Diagram for model types",
    font_size=16.5,
    width=1500,
    height=800,
    hovermode='x'
)
fig.show()

fig.write_html("C:/Users/U727148/Latent_Variable_Supplement/sankey_models_plot.html", include_plotlyjs="cdn")